Overview

Dataset statistics

Number of variables23
Number of observations68913
Missing cells215910
Missing cells (%)13.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.1 MiB
Average record size in memory184.0 B

Variable types

Numeric19
DateTime1
Categorical1
Text2

Alerts

importance1 has 7791 (11.3%) missing valuesMissing
importance2 has 7791 (11.3%) missing valuesMissing
score1 has 2004 (2.9%) missing valuesMissing
score2 has 2004 (2.9%) missing valuesMissing
xg1 has 32720 (47.5%) missing valuesMissing
xg2 has 32720 (47.5%) missing valuesMissing
nsxg1 has 32720 (47.5%) missing valuesMissing
nsxg2 has 32720 (47.5%) missing valuesMissing
adj_score1 has 32720 (47.5%) missing valuesMissing
adj_score2 has 32720 (47.5%) missing valuesMissing
importance1 has 5049 (7.3%) zerosZeros
importance2 has 5048 (7.3%) zerosZeros
score1 has 15637 (22.7%) zerosZeros
score2 has 21401 (31.1%) zerosZeros
adj_score1 has 8211 (11.9%) zerosZeros
adj_score2 has 11621 (16.9%) zerosZeros

Reproduction

Analysis started2024-06-12 02:05:52.614480
Analysis finished2024-06-12 02:06:28.916033
Duration36.3 seconds
Software versionydata-profiling v0.0.dev0
Download configurationconfig.json

Variables

season
Real number (ℝ)

Distinct8
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.6443
Minimum2016
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:29.001035image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2016
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2023
Range7
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.8541947
Coefficient of variation (CV)0.00091807985
Kurtosis-1.0517973
Mean2019.6443
Median Absolute Deviation (MAD)2
Skewness-0.070527478
Sum1.3917974 × 108
Variance3.438038
MonotonicityNot monotonic
2024-06-11T19:06:29.107033image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2021 11617
16.9%
2022 11562
16.8%
2019 11246
16.3%
2018 11224
16.3%
2020 10303
15.0%
2017 8243
12.0%
2023 2577
 
3.7%
2016 2141
 
3.1%
ValueCountFrequency (%)
2016 2141
 
3.1%
2017 8243
12.0%
2018 11224
16.3%
2019 11246
16.3%
2020 10303
15.0%
2021 11617
16.9%
2022 11562
16.8%
2023 2577
 
3.7%
ValueCountFrequency (%)
2023 2577
 
3.7%
2022 11562
16.8%
2021 11617
16.9%
2020 10303
15.0%
2019 11246
16.3%
2018 11224
16.3%
2017 8243
12.0%
2016 2141
 
3.1%

date
Date

Distinct2402
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size538.5 KiB
Minimum2016-07-09 00:00:00
Maximum2023-12-03 00:00:00
2024-06-11T19:06:29.233150image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:29.371698image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

league_id
Real number (ℝ)

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2248.0589
Minimum1818
Maximum10281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:29.495814image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1818
5-th percentile1832
Q11854
median1879
Q32160
95-th percentile4582
Maximum10281
Range8463
Interquartile range (IQR)306

Descriptive statistics

Standard deviation1087.7053
Coefficient of variation (CV)0.483842
Kurtosis22.580089
Mean2248.0589
Median Absolute Deviation (MAD)47
Skewness4.5209481
Sum1.5492049 × 108
Variance1183102.9
MonotonicityNot monotonic
2024-06-11T19:06:29.619815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
2412 3342
 
4.8%
1951 2998
 
4.4%
2160 2888
 
4.2%
1871 2807
 
4.1%
2414 2673
 
3.9%
2105 2660
 
3.9%
2411 2660
 
3.9%
1869 2660
 
3.9%
1854 2660
 
3.9%
1843 2660
 
3.9%
Other values (30) 40905
59.4%
ValueCountFrequency (%)
1818 869
 
1.3%
1820 1093
1.6%
1827 1154
1.7%
1832 1500
2.2%
1837 1065
1.5%
1843 2660
3.9%
1844 2280
3.3%
1845 2142
3.1%
1846 1836
2.7%
1849 1836
2.7%
ValueCountFrequency (%)
10281 282
 
0.4%
9541 83
 
0.1%
7921 733
 
1.1%
5641 2060
3.0%
4582 844
 
1.2%
2417 1332
 
1.9%
2414 2673
3.9%
2413 2629
3.8%
2412 3342
4.8%
2411 2660
3.9%

league
Categorical

Distinct40
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size538.5 KiB
English League Championship
 
3342
Major League Soccer
 
2998
United Soccer League
 
2888
Spanish Segunda Division
 
2807
English League Two
 
2673
Other values (35)
54205 

Length

Max length40
Median length27
Mean length20.727279
Min length13

Characters and Unicode

Total characters1428379
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFA Women's Super League
2nd rowFA Women's Super League
3rd rowFA Women's Super League
4th rowFA Women's Super League
5th rowFA Women's Super League

Common Values

ValueCountFrequency (%)
English League Championship 3342
 
4.8%
Major League Soccer 2998
 
4.4%
United Soccer League 2888
 
4.2%
Spanish Segunda Division 2807
 
4.1%
English League Two 2673
 
3.9%
Brasileiro Série A 2660
 
3.9%
Barclays Premier League 2660
 
3.9%
Spanish Primera Division 2660
 
3.9%
Italy Serie A 2660
 
3.9%
French Ligue 1 2660
 
3.9%
Other values (30) 40905
59.4%

Length

2024-06-11T19:06:29.745815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
league 27631
 
13.4%
division 9726
 
4.7%
english 8644
 
4.2%
primera 6919
 
3.4%
soccer 6730
 
3.3%
spanish 5467
 
2.7%
a 5320
 
2.6%
super 5202
 
2.5%
bundesliga 5132
 
2.5%
premier 5055
 
2.5%
Other values (63) 119884
58.3%

Most occurring characters

ValueCountFrequency (%)
e 172577
 
12.1%
136797
 
9.6%
i 131733
 
9.2%
a 105333
 
7.4%
r 90110
 
6.3%
n 79882
 
5.6%
u 66763
 
4.7%
s 65904
 
4.6%
g 65164
 
4.6%
o 44436
 
3.1%
Other values (40) 469680
32.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1428379
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 172577
 
12.1%
136797
 
9.6%
i 131733
 
9.2%
a 105333
 
7.4%
r 90110
 
6.3%
n 79882
 
5.6%
u 66763
 
4.7%
s 65904
 
4.6%
g 65164
 
4.6%
o 44436
 
3.1%
Other values (40) 469680
32.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1428379
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 172577
 
12.1%
136797
 
9.6%
i 131733
 
9.2%
a 105333
 
7.4%
r 90110
 
6.3%
n 79882
 
5.6%
u 66763
 
4.7%
s 65904
 
4.6%
g 65164
 
4.6%
o 44436
 
3.1%
Other values (40) 469680
32.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1428379
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 172577
 
12.1%
136797
 
9.6%
i 131733
 
9.2%
a 105333
 
7.4%
r 90110
 
6.3%
n 79882
 
5.6%
u 66763
 
4.7%
s 65904
 
4.6%
g 65164
 
4.6%
o 44436
 
3.1%
Other values (40) 469680
32.9%

team1
Text

Distinct888
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:29.951930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length24
Mean length11.482159
Min length2

Characters and Unicode

Total characters791270
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLiverpool Women
2nd rowArsenal Women
3rd rowChelsea FC Women
4th rowLiverpool Women
5th rowChelsea FC Women
ValueCountFrequency (%)
fc 6927
 
5.7%
city 2617
 
2.2%
united 2408
 
2.0%
town 1357
 
1.1%
real 1043
 
0.9%
st 823
 
0.7%
sc 787
 
0.6%
sv 539
 
0.4%
san 511
 
0.4%
new 494
 
0.4%
Other values (1170) 103963
85.6%
2024-06-11T19:06:30.322057image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 67777
 
8.6%
a 67333
 
8.5%
n 52689
 
6.7%
52556
 
6.6%
o 51085
 
6.5%
r 48894
 
6.2%
i 44481
 
5.6%
t 39225
 
5.0%
l 35760
 
4.5%
s 33744
 
4.3%
Other values (68) 297726
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 791270
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 67777
 
8.6%
a 67333
 
8.5%
n 52689
 
6.7%
52556
 
6.6%
o 51085
 
6.5%
r 48894
 
6.2%
i 44481
 
5.6%
t 39225
 
5.0%
l 35760
 
4.5%
s 33744
 
4.3%
Other values (68) 297726
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 791270
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 67777
 
8.6%
a 67333
 
8.5%
n 52689
 
6.7%
52556
 
6.6%
o 51085
 
6.5%
r 48894
 
6.2%
i 44481
 
5.6%
t 39225
 
5.0%
l 35760
 
4.5%
s 33744
 
4.3%
Other values (68) 297726
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 791270
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 67777
 
8.6%
a 67333
 
8.5%
n 52689
 
6.7%
52556
 
6.6%
o 51085
 
6.5%
r 48894
 
6.2%
i 44481
 
5.6%
t 39225
 
5.0%
l 35760
 
4.5%
s 33744
 
4.3%
Other values (68) 297726
37.6%

team2
Text

Distinct888
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:30.566162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length35
Median length24
Mean length11.48213
Min length2

Characters and Unicode

Total characters791268
Distinct characters78
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowReading
2nd rowNotts County Ladies
3rd rowBirmingham City
4th rowNotts County Ladies
5th rowArsenal Women
ValueCountFrequency (%)
fc 6907
 
5.7%
city 2599
 
2.1%
united 2431
 
2.0%
town 1357
 
1.1%
real 1039
 
0.9%
st 825
 
0.7%
sc 785
 
0.6%
sv 540
 
0.4%
new 528
 
0.4%
san 508
 
0.4%
Other values (1170) 103978
85.6%
2024-06-11T19:06:30.948162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 67821
 
8.6%
a 67349
 
8.5%
n 52676
 
6.7%
52584
 
6.6%
o 51072
 
6.5%
r 48886
 
6.2%
i 44479
 
5.6%
t 39199
 
5.0%
l 35699
 
4.5%
s 33701
 
4.3%
Other values (68) 297802
37.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 791268
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 67821
 
8.6%
a 67349
 
8.5%
n 52676
 
6.7%
52584
 
6.6%
o 51072
 
6.5%
r 48886
 
6.2%
i 44479
 
5.6%
t 39199
 
5.0%
l 35699
 
4.5%
s 33701
 
4.3%
Other values (68) 297802
37.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 791268
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 67821
 
8.6%
a 67349
 
8.5%
n 52676
 
6.7%
52584
 
6.6%
o 51072
 
6.5%
r 48886
 
6.2%
i 44479
 
5.6%
t 39199
 
5.0%
l 35699
 
4.5%
s 33701
 
4.3%
Other values (68) 297802
37.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 791268
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 67821
 
8.6%
a 67349
 
8.5%
n 52676
 
6.7%
52584
 
6.6%
o 51072
 
6.5%
r 48886
 
6.2%
i 44479
 
5.6%
t 39199
 
5.0%
l 35699
 
4.5%
s 33701
 
4.3%
Other values (68) 297802
37.6%

spi1
Real number (ℝ)

Distinct8663
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.386865
Minimum3.06
Maximum96.57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:31.088162image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.06
5-th percentile13.46
Q129.83
median41.39
Q356.21
95-th percentile77.53
Maximum96.57
Range93.51
Interquartile range (IQR)26.38

Descriptive statistics

Standard deviation18.988991
Coefficient of variation (CV)0.43766683
Kurtosis-0.41886538
Mean43.386865
Median Absolute Deviation (MAD)12.94
Skewness0.32803205
Sum2989919
Variance360.5818
MonotonicityNot monotonic
2024-06-11T19:06:31.224293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.27 45
 
0.1%
38.12 32
 
< 0.1%
42.09 32
 
< 0.1%
37.54 32
 
< 0.1%
40.36 31
 
< 0.1%
37.18 31
 
< 0.1%
47.87 31
 
< 0.1%
38.77 31
 
< 0.1%
57.95 30
 
< 0.1%
39.35 29
 
< 0.1%
Other values (8653) 68589
99.5%
ValueCountFrequency (%)
3.06 1
< 0.1%
3.08 1
< 0.1%
3.57 1
< 0.1%
3.73 1
< 0.1%
3.88 1
< 0.1%
3.89 1
< 0.1%
4.09 1
< 0.1%
4.1 1
< 0.1%
4.11 1
< 0.1%
4.13 1
< 0.1%
ValueCountFrequency (%)
96.57 1
< 0.1%
96.49 1
< 0.1%
96.46 1
< 0.1%
96.37 1
< 0.1%
96.35 1
< 0.1%
96.34 1
< 0.1%
96.32 1
< 0.1%
96.19 1
< 0.1%
96.13 1
< 0.1%
96.11 1
< 0.1%

spi2
Real number (ℝ)

Distinct8675
Distinct (%)12.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.344475
Minimum3.12
Maximum96.78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:31.358293image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum3.12
5-th percentile13.36
Q129.84
median41.29
Q356.11
95-th percentile77.59
Maximum96.78
Range93.66
Interquartile range (IQR)26.27

Descriptive statistics

Standard deviation18.9959
Coefficient of variation (CV)0.43825424
Kurtosis-0.41794318
Mean43.344475
Median Absolute Deviation (MAD)12.93
Skewness0.32863256
Sum2986997.8
Variance360.84422
MonotonicityNot monotonic
2024-06-11T19:06:31.497089image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42.27 52
 
0.1%
39.35 40
 
0.1%
37.1 31
 
< 0.1%
39.95 31
 
< 0.1%
42.09 30
 
< 0.1%
42.42 30
 
< 0.1%
43.1 30
 
< 0.1%
26.35 29
 
< 0.1%
45.11 29
 
< 0.1%
41.63 29
 
< 0.1%
Other values (8665) 68582
99.5%
ValueCountFrequency (%)
3.12 1
< 0.1%
3.14 1
< 0.1%
3.3 1
< 0.1%
3.46 1
< 0.1%
3.92 2
< 0.1%
4.04 2
< 0.1%
4.07 1
< 0.1%
4.08 1
< 0.1%
4.1 1
< 0.1%
4.14 1
< 0.1%
ValueCountFrequency (%)
96.78 1
< 0.1%
96.73 1
< 0.1%
96.69 1
< 0.1%
96.67 1
< 0.1%
96.65 1
< 0.1%
96.47 1
< 0.1%
96.41 1
< 0.1%
96.4 2
< 0.1%
96.37 1
< 0.1%
96.33 1
< 0.1%

prob1
Real number (ℝ)

Distinct8104
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.44181186
Minimum0.0225
Maximum0.9775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:31.636866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0225
5-th percentile0.1908
Q10.3416
median0.4327
Q30.533
95-th percentile0.7249
Maximum0.9775
Range0.955
Interquartile range (IQR)0.1914

Descriptive statistics

Standard deviation0.15616424
Coefficient of variation (CV)0.35346322
Kurtosis0.21849438
Mean0.44181186
Median Absolute Deviation (MAD)0.0953
Skewness0.33270663
Sum30446.58
Variance0.024387271
MonotonicityNot monotonic
2024-06-11T19:06:31.776866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.4072 42
 
0.1%
0.4229 38
 
0.1%
0.3798 33
 
< 0.1%
0.4486 31
 
< 0.1%
0.3892 31
 
< 0.1%
0.4304 31
 
< 0.1%
0.4168 31
 
< 0.1%
0.4775 31
 
< 0.1%
0.3565 30
 
< 0.1%
0.4169 30
 
< 0.1%
Other values (8094) 68585
99.5%
ValueCountFrequency (%)
0.0225 1
< 0.1%
0.027 1
< 0.1%
0.0271 1
< 0.1%
0.0281 1
< 0.1%
0.0283 2
< 0.1%
0.0307 1
< 0.1%
0.0311 1
< 0.1%
0.0315 1
< 0.1%
0.0327 1
< 0.1%
0.0337 1
< 0.1%
ValueCountFrequency (%)
0.9775 1
< 0.1%
0.9684 1
< 0.1%
0.9677 1
< 0.1%
0.9673 1
< 0.1%
0.9665 1
< 0.1%
0.9661 1
< 0.1%
0.9657 1
< 0.1%
0.9656 1
< 0.1%
0.9622 1
< 0.1%
0.961 1
< 0.1%

prob2
Real number (ℝ)

Distinct7338
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.30494922
Minimum0.0032
Maximum0.8995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:31.907975image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.0032
5-th percentile0.0913
Q10.21
median0.2898
Q30.3815
95-th percentile0.5758
Maximum0.8995
Range0.8963
Interquartile range (IQR)0.1715

Descriptive statistics

Standard deviation0.1427518
Coefficient of variation (CV)0.46811664
Kurtosis0.63035563
Mean0.30494922
Median Absolute Deviation (MAD)0.0849
Skewness0.6614533
Sum21014.966
Variance0.020378078
MonotonicityNot monotonic
2024-06-11T19:06:32.035977image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.3153 43
 
0.1%
0.2645 37
 
0.1%
0.2674 36
 
0.1%
0.2722 36
 
0.1%
0.2789 36
 
0.1%
0.2719 35
 
0.1%
0.2698 35
 
0.1%
0.2944 35
 
0.1%
0.3455 35
 
0.1%
0.2618 34
 
< 0.1%
Other values (7328) 68551
99.5%
ValueCountFrequency (%)
0.0032 1
< 0.1%
0.0037 1
< 0.1%
0.0039 1
< 0.1%
0.0043 1
< 0.1%
0.0048 1
< 0.1%
0.0053 1
< 0.1%
0.0054 1
< 0.1%
0.0058 2
< 0.1%
0.0059 2
< 0.1%
0.0062 1
< 0.1%
ValueCountFrequency (%)
0.8995 1
< 0.1%
0.8992 1
< 0.1%
0.8937 1
< 0.1%
0.8918 1
< 0.1%
0.8844 1
< 0.1%
0.8841 2
< 0.1%
0.8823 1
< 0.1%
0.8819 1
< 0.1%
0.8816 1
< 0.1%
0.8801 1
< 0.1%

probtie
Real number (ℝ)

Distinct3025
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.25323881
Minimum0
Maximum0.4537
Zeros304
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:32.162979image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.164
Q10.2352
median0.2605
Q30.2812
95-th percentile0.3121
Maximum0.4537
Range0.4537
Interquartile range (IQR)0.046

Descriptive statistics

Standard deviation0.046705435
Coefficient of variation (CV)0.18443238
Kurtosis4.8961234
Mean0.25323881
Median Absolute Deviation (MAD)0.0226
Skewness-1.536901
Sum17451.446
Variance0.0021813977
MonotonicityNot monotonic
2024-06-11T19:06:32.291974image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 304
 
0.4%
0.2639 120
 
0.2%
0.2729 114
 
0.2%
0.2635 106
 
0.2%
0.2661 105
 
0.2%
0.2669 104
 
0.2%
0.2604 103
 
0.1%
0.2692 102
 
0.1%
0.2659 101
 
0.1%
0.262 101
 
0.1%
Other values (3015) 67653
98.2%
ValueCountFrequency (%)
0 304
0.4%
0.0188 1
 
< 0.1%
0.0261 1
 
< 0.1%
0.0262 1
 
< 0.1%
0.027 1
 
< 0.1%
0.0284 2
 
< 0.1%
0.0285 1
 
< 0.1%
0.0296 1
 
< 0.1%
0.0313 1
 
< 0.1%
0.0321 1
 
< 0.1%
ValueCountFrequency (%)
0.4537 1
< 0.1%
0.4455 1
< 0.1%
0.4438 1
< 0.1%
0.4389 1
< 0.1%
0.4388 1
< 0.1%
0.4346 1
< 0.1%
0.4237 1
< 0.1%
0.4226 1
< 0.1%
0.4174 1
< 0.1%
0.4129 1
< 0.1%

proj_score1
Real number (ℝ)

Distinct361
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4941898
Minimum0.21
Maximum4.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:32.418976image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.21
5-th percentile0.92
Q11.22
median1.44
Q31.7
95-th percentile2.27
Maximum4.9
Range4.69
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.41828141
Coefficient of variation (CV)0.27993861
Kurtosis2.2642464
Mean1.4941898
Median Absolute Deviation (MAD)0.24
Skewness1.0429847
Sum102969.1
Variance0.17495934
MonotonicityNot monotonic
2024-06-11T19:06:32.551079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.38 859
 
1.2%
1.35 858
 
1.2%
1.33 847
 
1.2%
1.43 840
 
1.2%
1.34 837
 
1.2%
1.41 816
 
1.2%
1.44 814
 
1.2%
1.39 814
 
1.2%
1.36 814
 
1.2%
1.28 812
 
1.2%
Other values (351) 60602
87.9%
ValueCountFrequency (%)
0.21 1
< 0.1%
0.24 1
< 0.1%
0.25 1
< 0.1%
0.28 1
< 0.1%
0.3 1
< 0.1%
0.31 1
< 0.1%
0.35 1
< 0.1%
0.36 1
< 0.1%
0.37 1
< 0.1%
0.38 1
< 0.1%
ValueCountFrequency (%)
4.9 1
< 0.1%
4.41 1
< 0.1%
4.4 1
< 0.1%
4.3 1
< 0.1%
4.22 1
< 0.1%
4.21 1
< 0.1%
4.2 1
< 0.1%
4.09 1
< 0.1%
4.07 1
< 0.1%
4.03 1
< 0.1%

proj_score2
Real number (ℝ)

Distinct328
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1750305
Minimum0.2
Maximum4.13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:32.837085image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile0.59
Q10.91
median1.12
Q31.39
95-th percentile1.94
Maximum4.13
Range3.93
Interquartile range (IQR)0.48

Descriptive statistics

Standard deviation0.41286587
Coefficient of variation (CV)0.35136607
Kurtosis1.7953176
Mean1.1750305
Median Absolute Deviation (MAD)0.24
Skewness0.86543294
Sum80974.88
Variance0.17045823
MonotonicityNot monotonic
2024-06-11T19:06:32.974079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.09 882
 
1.3%
1.04 859
 
1.2%
1.03 846
 
1.2%
1.12 843
 
1.2%
1.02 837
 
1.2%
0.95 827
 
1.2%
1.05 826
 
1.2%
1.08 825
 
1.2%
1.13 819
 
1.2%
1 816
 
1.2%
Other values (318) 60533
87.8%
ValueCountFrequency (%)
0.2 212
0.3%
0.21 31
 
< 0.1%
0.22 29
 
< 0.1%
0.23 19
 
< 0.1%
0.24 19
 
< 0.1%
0.25 37
 
0.1%
0.26 35
 
0.1%
0.27 33
 
< 0.1%
0.28 31
 
< 0.1%
0.29 38
 
0.1%
ValueCountFrequency (%)
4.13 1
< 0.1%
4.01 1
< 0.1%
3.9 1
< 0.1%
3.88 1
< 0.1%
3.77 1
< 0.1%
3.75 1
< 0.1%
3.71 1
< 0.1%
3.66 1
< 0.1%
3.63 1
< 0.1%
3.62 1
< 0.1%

importance1
Real number (ℝ)

MISSING  ZEROS 

Distinct1001
Distinct (%)1.6%
Missing7791
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean31.660207
Minimum0
Maximum100
Zeros5049
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:33.113193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111.1
median26.3
Q345.9
95-th percentile90.6
Maximum100
Range100
Interquartile range (IQR)34.8

Descriptive statistics

Standard deviation26.349662
Coefficient of variation (CV)0.83226434
Kurtosis0.2351391
Mean31.660207
Median Absolute Deviation (MAD)17.2
Skewness0.90922942
Sum1935135.2
Variance694.30468
MonotonicityNot monotonic
2024-06-11T19:06:33.246193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5049
 
7.3%
100 2350
 
3.4%
0.1 461
 
0.7%
0.2 289
 
0.4%
0.3 266
 
0.4%
0.4 207
 
0.3%
0.7 178
 
0.3%
0.5 174
 
0.3%
0.6 153
 
0.2%
0.8 152
 
0.2%
Other values (991) 51843
75.2%
(Missing) 7791
 
11.3%
ValueCountFrequency (%)
0 5049
7.3%
0.1 461
 
0.7%
0.2 289
 
0.4%
0.3 266
 
0.4%
0.4 207
 
0.3%
0.5 174
 
0.3%
0.6 153
 
0.2%
0.7 178
 
0.3%
0.8 152
 
0.2%
0.9 125
 
0.2%
ValueCountFrequency (%)
100 2350
3.4%
99.9 2
 
< 0.1%
99.8 7
 
< 0.1%
99.7 3
 
< 0.1%
99.6 7
 
< 0.1%
99.5 4
 
< 0.1%
99.4 8
 
< 0.1%
99.3 7
 
< 0.1%
99.2 7
 
< 0.1%
99.1 4
 
< 0.1%

importance2
Real number (ℝ)

MISSING  ZEROS 

Distinct1000
Distinct (%)1.6%
Missing7791
Missing (%)11.3%
Infinite0
Infinite (%)0.0%
Mean31.078451
Minimum0
Maximum100
Zeros5048
Zeros (%)7.3%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:33.376409image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.7
median25.5
Q345.3
95-th percentile88.795
Maximum100
Range100
Interquartile range (IQR)34.6

Descriptive statistics

Standard deviation26.114623
Coefficient of variation (CV)0.8402807
Kurtosis0.3016583
Mean31.078451
Median Absolute Deviation (MAD)16.9
Skewness0.93901538
Sum1899577.1
Variance681.97353
MonotonicityNot monotonic
2024-06-11T19:06:33.509405image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5048
 
7.3%
100 2226
 
3.2%
0.1 458
 
0.7%
0.2 340
 
0.5%
0.3 232
 
0.3%
0.4 226
 
0.3%
0.5 170
 
0.2%
0.9 164
 
0.2%
0.8 161
 
0.2%
0.6 158
 
0.2%
Other values (990) 51939
75.4%
(Missing) 7791
 
11.3%
ValueCountFrequency (%)
0 5048
7.3%
0.1 458
 
0.7%
0.2 340
 
0.5%
0.3 232
 
0.3%
0.4 226
 
0.3%
0.5 170
 
0.2%
0.6 158
 
0.2%
0.7 148
 
0.2%
0.8 161
 
0.2%
0.9 164
 
0.2%
ValueCountFrequency (%)
100 2226
3.2%
99.9 9
 
< 0.1%
99.8 9
 
< 0.1%
99.7 6
 
< 0.1%
99.6 6
 
< 0.1%
99.5 7
 
< 0.1%
99.4 4
 
< 0.1%
99.3 2
 
< 0.1%
99.2 7
 
< 0.1%
99.1 3
 
< 0.1%

score1
Real number (ℝ)

MISSING  ZEROS 

Distinct12
Distinct (%)< 0.1%
Missing2004
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean1.4982289
Minimum0
Maximum11
Zeros15637
Zeros (%)22.7%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:33.622518image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2724521
Coefficient of variation (CV)0.84930418
Kurtosis1.1534875
Mean1.4982289
Median Absolute Deviation (MAD)1
Skewness0.95548959
Sum100245
Variance1.6191343
MonotonicityNot monotonic
2024-06-11T19:06:33.720072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1 22235
32.3%
2 16167
23.5%
0 15637
22.7%
3 8084
 
11.7%
4 3158
 
4.6%
5 1146
 
1.7%
6 356
 
0.5%
7 95
 
0.1%
8 22
 
< 0.1%
9 6
 
< 0.1%
Other values (2) 3
 
< 0.1%
(Missing) 2004
 
2.9%
ValueCountFrequency (%)
0 15637
22.7%
1 22235
32.3%
2 16167
23.5%
3 8084
 
11.7%
4 3158
 
4.6%
5 1146
 
1.7%
6 356
 
0.5%
7 95
 
0.1%
8 22
 
< 0.1%
9 6
 
< 0.1%
ValueCountFrequency (%)
11 1
 
< 0.1%
10 2
 
< 0.1%
9 6
 
< 0.1%
8 22
 
< 0.1%
7 95
 
0.1%
6 356
 
0.5%
5 1146
 
1.7%
4 3158
 
4.6%
3 8084
11.7%
2 16167
23.5%

score2
Real number (ℝ)

MISSING  ZEROS 

Distinct11
Distinct (%)< 0.1%
Missing2004
Missing (%)2.9%
Infinite0
Infinite (%)0.0%
Mean1.1837421
Minimum0
Maximum13
Zeros21401
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:33.817076image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile3
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.1385139
Coefficient of variation (CV)0.96179216
Kurtosis1.6551904
Mean1.1837421
Median Absolute Deviation (MAD)1
Skewness1.0977715
Sum79203
Variance1.2962138
MonotonicityNot monotonic
2024-06-11T19:06:33.920191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 23686
34.4%
0 21401
31.1%
2 13601
19.7%
3 5580
 
8.1%
4 1915
 
2.8%
5 521
 
0.8%
6 150
 
0.2%
7 39
 
0.1%
8 11
 
< 0.1%
9 4
 
< 0.1%
(Missing) 2004
 
2.9%
ValueCountFrequency (%)
0 21401
31.1%
1 23686
34.4%
2 13601
19.7%
3 5580
 
8.1%
4 1915
 
2.8%
5 521
 
0.8%
6 150
 
0.2%
7 39
 
0.1%
8 11
 
< 0.1%
9 4
 
< 0.1%
ValueCountFrequency (%)
13 1
 
< 0.1%
9 4
 
< 0.1%
8 11
 
< 0.1%
7 39
 
0.1%
6 150
 
0.2%
5 521
 
0.8%
4 1915
 
2.8%
3 5580
 
8.1%
2 13601
19.7%
1 23686
34.4%

xg1
Real number (ℝ)

MISSING 

Distinct543
Distinct (%)1.5%
Missing32720
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean1.499693
Minimum0
Maximum7.07
Zeros6
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:34.036189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q10.88
median1.37
Q31.97
95-th percentile3.04
Maximum7.07
Range7.07
Interquartile range (IQR)1.09

Descriptive statistics

Standard deviation0.83506732
Coefficient of variation (CV)0.5568255
Kurtosis1.5105424
Mean1.499693
Median Absolute Deviation (MAD)0.53
Skewness0.99687356
Sum54278.39
Variance0.69733743
MonotonicityNot monotonic
2024-06-11T19:06:34.171190image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.95 209
 
0.3%
1.47 208
 
0.3%
1.17 204
 
0.3%
1.55 203
 
0.3%
1.14 201
 
0.3%
0.87 200
 
0.3%
1.12 200
 
0.3%
1.15 200
 
0.3%
1.27 199
 
0.3%
1.01 198
 
0.3%
Other values (533) 34171
49.6%
(Missing) 32720
47.5%
ValueCountFrequency (%)
0 6
< 0.1%
0.01 1
 
< 0.1%
0.02 2
 
< 0.1%
0.03 5
 
< 0.1%
0.04 4
 
< 0.1%
0.05 6
< 0.1%
0.06 13
< 0.1%
0.07 7
< 0.1%
0.08 9
< 0.1%
0.09 13
< 0.1%
ValueCountFrequency (%)
7.07 1
< 0.1%
7.04 1
< 0.1%
6.72 1
< 0.1%
6.5 1
< 0.1%
6.21 1
< 0.1%
6.19 1
< 0.1%
6.12 1
< 0.1%
6.07 1
< 0.1%
6.03 1
< 0.1%
6.02 1
< 0.1%

xg2
Real number (ℝ)

MISSING 

Distinct479
Distinct (%)1.3%
Missing32720
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean1.1875752
Minimum0
Maximum8.27
Zeros19
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:34.306189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.25
Q10.63
median1.05
Q31.59
95-th percentile2.6
Maximum8.27
Range8.27
Interquartile range (IQR)0.96

Descriptive statistics

Standard deviation0.74540743
Coefficient of variation (CV)0.62767176
Kurtosis2.0126921
Mean1.1875752
Median Absolute Deviation (MAD)0.47
Skewness1.1398907
Sum42981.91
Variance0.55563224
MonotonicityNot monotonic
2024-06-11T19:06:34.437191image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.64 265
 
0.4%
0.9 238
 
0.3%
0.72 236
 
0.3%
0.74 236
 
0.3%
0.73 235
 
0.3%
0.59 232
 
0.3%
1.12 231
 
0.3%
1 231
 
0.3%
0.92 227
 
0.3%
0.82 227
 
0.3%
Other values (469) 33835
49.1%
(Missing) 32720
47.5%
ValueCountFrequency (%)
0 19
 
< 0.1%
0.01 7
 
< 0.1%
0.02 4
 
< 0.1%
0.03 17
 
< 0.1%
0.04 16
 
< 0.1%
0.05 23
< 0.1%
0.06 34
< 0.1%
0.07 30
< 0.1%
0.08 46
0.1%
0.09 55
0.1%
ValueCountFrequency (%)
8.27 1
< 0.1%
6.2 1
< 0.1%
5.99 1
< 0.1%
5.93 1
< 0.1%
5.9 1
< 0.1%
5.85 1
< 0.1%
5.61 1
< 0.1%
5.6 1
< 0.1%
5.51 1
< 0.1%
5.41 1
< 0.1%

nsxg1
Real number (ℝ)

MISSING 

Distinct473
Distinct (%)1.3%
Missing32720
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean1.388203
Minimum0
Maximum6.89
Zeros7
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:34.563189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.52
Q10.93
median1.3
Q31.73
95-th percentile2.57
Maximum6.89
Range6.89
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.64830143
Coefficient of variation (CV)0.46700767
Kurtosis2.6672777
Mean1.388203
Median Absolute Deviation (MAD)0.39
Skewness1.1133256
Sum50243.23
Variance0.42029475
MonotonicityNot monotonic
2024-06-11T19:06:34.707193image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.11 293
 
0.4%
1.31 276
 
0.4%
1.05 272
 
0.4%
1.07 271
 
0.4%
1.09 270
 
0.4%
1.15 267
 
0.4%
1.18 267
 
0.4%
1.29 267
 
0.4%
0.98 267
 
0.4%
1.44 266
 
0.4%
Other values (463) 33477
48.6%
(Missing) 32720
47.5%
ValueCountFrequency (%)
0 7
< 0.1%
0.02 2
 
< 0.1%
0.03 2
 
< 0.1%
0.05 3
 
< 0.1%
0.06 1
 
< 0.1%
0.07 6
< 0.1%
0.08 3
 
< 0.1%
0.09 7
< 0.1%
0.1 8
< 0.1%
0.11 4
< 0.1%
ValueCountFrequency (%)
6.89 1
< 0.1%
6.58 1
< 0.1%
6.19 1
< 0.1%
6.1 1
< 0.1%
6.08 1
< 0.1%
5.96 1
< 0.1%
5.72 1
< 0.1%
5.69 1
< 0.1%
5.57 1
< 0.1%
5.42 1
< 0.1%

nsxg2
Real number (ℝ)

MISSING 

Distinct405
Distinct (%)1.1%
Missing32720
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean1.1360887
Minimum0
Maximum7.17
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:34.842189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.37
Q10.73
median1.05
Q31.44
95-th percentile2.21
Maximum7.17
Range7.17
Interquartile range (IQR)0.71

Descriptive statistics

Standard deviation0.57843576
Coefficient of variation (CV)0.50914663
Kurtosis2.533851
Mean1.1360887
Median Absolute Deviation (MAD)0.35
Skewness1.1222308
Sum41118.46
Variance0.33458793
MonotonicityNot monotonic
2024-06-11T19:06:34.974189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.75 324
 
0.5%
0.89 309
 
0.4%
0.9 308
 
0.4%
0.93 304
 
0.4%
0.87 303
 
0.4%
0.95 300
 
0.4%
1.06 297
 
0.4%
0.82 296
 
0.4%
0.79 296
 
0.4%
0.92 296
 
0.4%
Other values (395) 33160
48.1%
(Missing) 32720
47.5%
ValueCountFrequency (%)
0 10
< 0.1%
0.01 7
 
< 0.1%
0.02 5
 
< 0.1%
0.03 6
 
< 0.1%
0.04 7
 
< 0.1%
0.05 8
< 0.1%
0.06 12
< 0.1%
0.07 17
< 0.1%
0.08 16
< 0.1%
0.09 18
< 0.1%
ValueCountFrequency (%)
7.17 1
 
< 0.1%
5.92 1
 
< 0.1%
5.68 1
 
< 0.1%
5.62 1
 
< 0.1%
5.56 1
 
< 0.1%
4.9 1
 
< 0.1%
4.62 1
 
< 0.1%
4.57 1
 
< 0.1%
4.55 1
 
< 0.1%
4.49 3
< 0.1%

adj_score1
Real number (ℝ)

MISSING  ZEROS 

Distinct518
Distinct (%)1.4%
Missing32720
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean1.515936
Minimum0
Maximum9.15
Zeros8211
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:35.108189image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.05
median1.05
Q32.1
95-th percentile3.83
Maximum9.15
Range9.15
Interquartile range (IQR)1.05

Descriptive statistics

Standard deviation1.2386263
Coefficient of variation (CV)0.81707031
Kurtosis1.0266031
Mean1.515936
Median Absolute Deviation (MAD)1.05
Skewness0.86557186
Sum54866.27
Variance1.534195
MonotonicityNot monotonic
2024-06-11T19:06:35.237320image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.05 11638
 
16.9%
0 8211
 
11.9%
2.1 6902
 
10.0%
3.15 2275
 
3.3%
4.2 523
 
0.8%
0.84 402
 
0.6%
1.58 353
 
0.5%
2.63 336
 
0.5%
1.89 257
 
0.4%
1.68 181
 
0.3%
Other values (508) 5115
 
7.4%
(Missing) 32720
47.5%
ValueCountFrequency (%)
0 8211
11.9%
0.84 402
 
0.6%
1.05 11638
16.9%
1.26 22
 
< 0.1%
1.28 4
 
< 0.1%
1.3 1
 
< 0.1%
1.31 1
 
< 0.1%
1.33 1
 
< 0.1%
1.34 2
 
< 0.1%
1.35 3
 
< 0.1%
ValueCountFrequency (%)
9.15 1
< 0.1%
8.95 1
< 0.1%
8.82 1
< 0.1%
8.76 1
< 0.1%
8.62 1
< 0.1%
8.4 1
< 0.1%
8.09 1
< 0.1%
8.03 1
< 0.1%
8.01 1
< 0.1%
7.97 2
< 0.1%

adj_score2
Real number (ℝ)

MISSING  ZEROS 

Distinct438
Distinct (%)1.2%
Missing32720
Missing (%)47.5%
Infinite0
Infinite (%)0.0%
Mean1.1932841
Minimum0
Maximum11.05
Zeros11621
Zeros (%)16.9%
Negative0
Negative (%)0.0%
Memory size538.5 KiB
2024-06-11T19:06:35.366317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.05
Q32.1
95-th percentile3.15
Maximum11.05
Range11.05
Interquartile range (IQR)2.1

Descriptive statistics

Standard deviation1.1231236
Coefficient of variation (CV)0.9412039
Kurtosis1.1825136
Mean1.1932841
Median Absolute Deviation (MAD)1.05
Skewness0.98060211
Sum43188.53
Variance1.2614067
MonotonicityNot monotonic
2024-06-11T19:06:35.499317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.05 12480
 
18.1%
0 11621
 
16.9%
2.1 6042
 
8.8%
3.15 1669
 
2.4%
0.84 378
 
0.5%
4.2 342
 
0.5%
2.63 252
 
0.4%
1.58 206
 
0.3%
1.89 194
 
0.3%
1.68 135
 
0.2%
Other values (428) 2874
 
4.2%
(Missing) 32720
47.5%
ValueCountFrequency (%)
0 11621
16.9%
0.84 378
 
0.5%
1.05 12480
18.1%
1.26 15
 
< 0.1%
1.27 1
 
< 0.1%
1.29 1
 
< 0.1%
1.3 1
 
< 0.1%
1.32 1
 
< 0.1%
1.33 2
 
< 0.1%
1.34 1
 
< 0.1%
ValueCountFrequency (%)
11.05 1
< 0.1%
8.4 1
< 0.1%
7.93 1
< 0.1%
7.85 1
< 0.1%
7.23 1
< 0.1%
7.2 1
< 0.1%
7.1 1
< 0.1%
7.05 1
< 0.1%
6.92 1
< 0.1%
6.76 2
< 0.1%

Interactions

2024-06-11T19:06:26.257938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:54.176720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:56.179681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:57.876837image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:59.584065image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:01.465935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:03.210420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:04.928592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:06.802731image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:08.538734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:10.340182image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:12.222103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:13.949103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:15.647969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:17.538601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:19.296309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:20.948829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:22.846072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:24.606833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:26.354937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:54.316723image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:56.277681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:57.975834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:59.693172image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:01.566935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:03.305420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:05.025593image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:06.900726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:08.649734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:10.444739image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:12.321102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:14.045103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:15.748969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:17.639601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:19.389309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:21.047830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:22.945072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:24.701833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:26.438938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:54.412720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:56.362681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:58.061834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:59.784258image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:01.655937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:03.393546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:05.111594image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:06.995726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:08.740734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:10.530737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:12.412690image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:14.134103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:15.838973image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:17.727601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:19.470309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:21.133834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:23.035776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:24.783834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:26.525937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:54.506720image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:56.449682image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:58.149834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:59.869775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:01.749068image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:03.480546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:05.199595image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:07.082726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:08.832734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:10.629737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:12.500860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:14.221102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:15.928969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:17.815601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:19.554309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:21.223829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:23.126603image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:24.868833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:26.614937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:54.709856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:56.537683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:58.237835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:59.958776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:01.842075image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:03.570551image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:05.286715image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:07.169726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:08.927734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:10.717865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:12.602863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:14.309103image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:16.018969image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:17.904601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:19.651309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:21.312829image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:23.216604image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:24.951833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:26.702938image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:54.804857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:56.632681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:58.330834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:00.198776image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:01.934957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:03.662546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:05.374716image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:07.258728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:09.021735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:10.808865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:12.696860image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:14.396104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:16.108968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:17.994601image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:19.737309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:21.403828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:23.307602image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:25.037833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:26.785937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:54.895856image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:56.717681image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:58.418834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:00.284775image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:02.020957image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:03.746548image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:05.458717image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:07.343726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:09.112735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:10.893866image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:12.783861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:14.482104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:16.197079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2024-06-11T19:06:01.292935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:03.035420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:04.755592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:06.620726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:08.356734image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:10.155868image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:12.049107image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:13.778104image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:15.470968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:17.366497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:19.121309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:20.781451image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:22.673072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:24.429833image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:26.093937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:27.873925image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:56.086683image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:57.789835image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:05:59.494066image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:01.377935image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:03.119420image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:04.837592image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:06.709727image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:08.444735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:10.242864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:12.132102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:13.860102image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:15.554968image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:17.450496image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:19.206309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:20.862403image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:22.759072image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:24.515834image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2024-06-11T19:06:26.175937image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Missing values

2024-06-11T19:06:28.184921image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2024-06-11T19:06:28.520034image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

seasondateleague_idleagueteam1team2spi1spi2prob1prob2probtieproj_score1proj_score2importance1importance2score1score2xg1xg2nsxg1nsxg2adj_score1adj_score2
020162016-07-097921FA Women's Super LeagueLiverpool WomenReading51.5650.420.43890.27670.28441.391.05NaNNaN2.00.0NaNNaNNaNNaNNaNNaN
120162016-07-107921FA Women's Super LeagueArsenal WomenNotts County Ladies46.6154.030.35720.36080.28191.271.28NaNNaN2.00.0NaNNaNNaNNaNNaNNaN
220162016-07-107921FA Women's Super LeagueChelsea FC WomenBirmingham City59.8554.640.47990.24870.27141.531.03NaNNaN1.01.0NaNNaNNaNNaNNaNNaN
320162016-07-167921FA Women's Super LeagueLiverpool WomenNotts County Ladies53.0052.350.42890.26990.30131.270.94NaNNaN0.00.0NaNNaNNaNNaNNaNNaN
420162016-07-177921FA Women's Super LeagueChelsea FC WomenArsenal Women59.4360.990.41240.31570.27191.451.24NaNNaN1.02.0NaNNaNNaNNaNNaNNaN
520162016-07-247921FA Women's Super LeagueReadingBirmingham City50.7555.030.38210.32000.29791.221.09NaNNaN1.01.0NaNNaNNaNNaNNaNNaN
620162016-07-247921FA Women's Super LeagueNotts County LadiesManchester City Women48.1360.150.30820.38880.30301.041.20NaNNaN1.05.0NaNNaNNaNNaNNaNNaN
720162016-07-317921FA Women's Super LeagueReadingNotts County Ladies50.6252.630.40680.30330.28991.311.09NaNNaN1.01.0NaNNaNNaNNaNNaNNaN
820162016-07-317921FA Women's Super LeagueArsenal WomenLiverpool Women48.3248.460.43500.31000.25501.641.35NaNNaN1.02.0NaNNaNNaNNaNNaNNaN
920162016-08-037921FA Women's Super LeagueReadingManchester City Women50.4163.200.30610.41980.27421.201.45NaNNaN1.02.0NaNNaNNaNNaNNaNNaN
seasondateleague_idleagueteam1team2spi1spi2prob1prob2probtieproj_score1proj_score2importance1importance2score1score2xg1xg2nsxg1nsxg2adj_score1adj_score2
6890320232023-12-032105Brasileiro Série AVasco da GamaBragantino51.0253.580.44560.30730.24721.651.33NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6890420232023-12-032105Brasileiro Série ACuiabaAtlético Paranaense42.2752.130.36510.35520.27971.241.22NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6890520232023-12-032105Brasileiro Série AFluminenseGrêmio56.2947.170.57020.20710.22271.971.10NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6890620232023-12-032105Brasileiro Série ABahíaAtletico Mineiro45.5164.580.27870.45020.27121.071.43NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6890720232023-12-032105Brasileiro Série AGoiásAmérica Mineiro39.5945.110.40940.32930.26131.461.28NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6890820232023-12-032105Brasileiro Série ACruzeiroPalmeiras49.6669.020.27410.47370.25221.181.62NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6890920232023-12-032105Brasileiro Série ASão PauloFlamengo58.7166.270.39360.35930.24711.561.48NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6891020232023-12-032105Brasileiro Série ASantosFortaleza50.6653.060.43950.27240.28811.300.96NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6891120232023-12-032105Brasileiro Série AInternacionalBotafogo54.2057.950.42830.29660.27521.381.10NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6891220232023-12-032105Brasileiro Série ACoritibaCorinthians39.3551.010.34810.36300.28881.151.18NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN